Prize: an R package for prioritization estimation based on analytic hierarchy process
|
|
- Maximillian Kennedy
- 6 years ago
- Views:
Transcription
1 Prize: an R package for prioritization estimation based on analytic hierarchy process Daryanaz Dargahi October 30, 2017 daryanazdargahi@gmail.com Contents 1 Licensing 2 2 Overview 2 3 Relative AHP Defining the problem and determining the criteria, subcriteria, and alternatives Structuring the decision hierarchy Constructing pairwise comparison matrices Aggregating individual judgments into a group judgment Estimating and visualizing priorities Rating AHP Defining a rating scale and obtaining alternatives priorities
2 1 Licensing Under the Artistic License, you are free to use and redistribute this software. 2 Overview The high throughput studies often produce large amounts of numerous genes and proteins of interest. While it is difficult to study and validate all of them. In order to narrow down such lists, one approach is to use a series of criteria to rank and prioritize the potential candidates based on how well they meet the research goal. Analytic hierarchy process (AHP) [1] is one of the most popular group decision-making techniques for ranking and prioritizing alternatives when multiple criteria must be considered. It provides a comprehensive and rational framework to address complicated decisions by modeling the problem in a hierarchical structure, showing the relationships of the goal, objectives (criteria and subcriteria), and alternatives. AHP has unique advantages when communication among team members is impeded by their different specializations or perspectives. It also enables decision makers to evaluate decision alternatives when important elements of the decision are difficult to quantify or compare. The AHP technique uses pairwise comparisons to measure the impact of items on one level of the hierarchy on the next higher level. It has two models for arriving at a ranking of alternatives. (A) The relative model, where alternatives are compared in a pairwise manner regarding their ability to achieve each of the criteria. (B) The rating model is often used when the number of alternatives is large, or if the possibility of adding or deleting alternatives exists [2]. This model requires establishing a series of rating scales (categories) for each criterion. These scales must be pairwise compared to determine the relative importance of each rating category, and then alternatives are evaluated one at a time by selecting the appropriate rating category for each criterion. Here, we introduce an R package for AHP, Prize. Prize offers the implementation of both relative and rating AHP models. In order to rank and prioritize a set of alternatives with AHP, decision makers must take four steps: 1. Define the problem and determine the criteria, subcriteria, and alternatives 2. Structure the decision hierarchy 3. Construct pairwise comparison matrices 4. Estimate and visualize priorities In the following, we describe a brief example use case for Prize in translational oncology. 2
3 3 Relative AHP 3.1 Defining the problem and determining the criteria, subcriteria, and alternatives Assume a scenario that a group of scientists identified 10 genes that are being differentially expressed (DE) in tumor tissues in comparison to healthy tissues. They are interested in ranking and prioritizing these genes based on their potential role as a tumor marker or therapeutic target. They decide to consider the (1) gene expression profile in tumor tissue, (2) gene expression profile in healthy tissue, (3) frequency of being DE, and (4) epitopes as the criteria for making their decision. They also subdivide the epitope criterion into the size and number of extracellular regions. 3.2 Structuring the decision hierarchy The scientists form their decision hierarchy as follows; > require(prize) > require(diagram) > mat <- matrix(nrow = 7, ncol = 2, data = NA) > mat[,1] <- c('0', '1','2','3','4','4.1','4.2') > mat[,2] <- c('prioritization_of_de_genes','tumor_expression','normal_expression', + 'Frequency', 'Epitopes', 'Number_of_epitopes', 'Size_of_epitopes') > mat [,1] [,2] [1,] "0" "Prioritization_of_DE_genes" [2,] "1" "Tumor_expression" [3,] "2" "Normal_expression" [4,] "3" "Frequency" [5,] "4" "Epitopes" [6,] "4.1" "Number_of_epitopes" [7,] "4.2" "Size_of_epitopes" > ahplot(mat, fontsize = 0.7, cradx = 0.11,sradx = 0.12, cirx= 0.18, ciry = 0.07) 3
4 Prioritization_of_DE_genes Tumor_expression Normal_expression Frequency Epitopes Number_of_epitopes Size_of_epitopes 3.3 Constructing pairwise comparison matrices Each scientist (decision maker) investigates the values of the decision elements in the hierarchy, and incorporates their judgments by performing a pairwise comparison of these elements. Each decision element in the upper level is used to compare the elements of an immediate inferior level of the hierarchy with respect to the former. That is, the alternatives are compared with respect to the subcriteria, the subcriteria are compared with respect to the criteria and the criteria are compared with respect to the goal. Therefore, each decision maker constructs a set of pairwise comparison matrices reflecting how important decision elements are to them with respect to the goal. Pairwise comparison matrices are built from the comparison between elements, based on the Saaty fundamental scale [3]. 4
5 Table 1: Saaty fundamental scale for pairwise comparison [3] Intensity of Definition Explanation importance 1 Equal importance Two elements contribute equally to the objective 3 Moderate importance Experience and judgement slightly favor one element over an other 5 Strong importance Experience and judgement strongly favor one element over an other 7 Very strong importance 9 Extreme importance One element is favored very strongly over an other, its dominance is demonstrated in practice The evidence favoring one element over another is of the highest possible order of affirmation Intensities of 2,4,6, and 8 can be used to express intermediate values. Intensitise 1.1, 1.2, 1.3, etc. can be used for elements that are very close in importance For instance, to pairwise compare the criteria a total of six comparisons must be done, including Tumor expression/normal expression, Tumor expression/frequency, Tumor expression/epitope, Normal expression/frequency, Normal expression/epitope, and Frequency/Epitope. The criteria pairwise comparison matrix (PCM) is shown below. > pcm <- read.table(system.file('extdata','ind1.tsv',package = 'Prize'), + sep = '\t', header = TRUE, row.names = 1) > pcm Tumor_expression Normal_expression Frequency Epitopes Tumor_expression Normal_expression NA Frequency NA NA 1 2 Epitopes NA NA NA 1 The ahmatrix function completes a pairwise comparison matrix by converting the triangular matrix into a square matrix, where diagonal values are equal 1 and pcm[j,i] = 1/pcm[i,j]. 5
6 > pcm <- ahmatrix(pcm) > ahp_matrix(pcm) Tumor_expression Normal_expression Frequency Epitopes Tumor_expression Normal_expression Frequency Epitopes Aggregating individual judgments into a group judgment Once the individual PCMs are available, gaggregate function could be used to combine the opinions of various decision makers into an overall opinion for the group. gaggregate offers two aggregation methods including aggregation of individual judgments (AIJ - geometric mean) and aggregation of individual priorities (AIP - using arithmetic mean) [4]. If decision makers have different expertise or perspectives, in order to reflect that in the group judgment, one can use a weighted AIJ or AIP, by simply providing a weight for each decision maker. > mat = matrix(nrow = 4, ncol = 1, data = NA) > mat[,1] = c(system.file('extdata','ind1.tsv',package = 'Prize'), + system.file('extdata','ind2.tsv',package = 'Prize'), + system.file('extdata','ind3.tsv',package = 'Prize'), + system.file('extdata','ind4.tsv',package = 'Prize')) > rownames(mat) = c('ind1','ind2','ind3', 'ind4') > colnames(mat) = c('individual_judgement') > # non-weighted AIJ > res = gaggregate(srcfile = mat, method = 'geometric', simulation = 500) > # aggregated group judgement using non-weighted AIJ > AIJ(res) Tumor_expression Normal_expression Frequency Epitopes Tumor_expression Normal_expression Frequency Epitopes > # consistency ratio of the aggregated group judgement > GCR(res) [1] The distance among individual and group judgments can be visualized using the dplot function. dplot uses a classical multidimensional scaling (MDS) approach [5] to compute the distance among individual and group priorities. 6
7 > require(ggplot2) > # Distance between individual opinions and the aggregated group judgement > dplot(ip(res)) 0.03 ind ind Coordinate Group judgement ind1 ind Coordinate 1 The consistency ratio of individual judgments can be visualized using the crplot function. If the consistency ratio is equal or smaller than 0.1, then the decision is considered to be consistent. > # Consistency ratio of individal opinions > crplot(icr(res), angle = 45) 7
8 ICR Condition Failed Pass ind1 ind2 ind3 ind4 ID 3.4 Estimating and visualizing priorities In order to obtain the priorities of decision elements to generate the final alternatives priorities, local and global priorities are required to be obtained from the comparison matrices. Local priorities are determined by computing the maximum eigenvalue of the PCMs. The local priorities are then used to ponder the priorities of the immediately lower level for each element. The global priorities are obtained by multiplying the local priorities of the elements by the global priority of their above element. The total priorities of the alternatives are found by the addition of alternatives global prioritiese. The pipeline function computes local and global priorities, as well as final prioritization values. P ipeline can simply be called by a matrix including the 8
9 problem hierarchy and group PCMs. The scientists use the following matrix (mat) to call the pipeline function; > require(stringr) > mat <- matrix(nrow = 7, ncol = 3, data = NA) > mat[,1] <- c('0', '1','2','3','4','4.1','4.2') > mat[,2] <- c('prioritization_of_de_genes','tumor_expression','normal_expression', + 'Frequency', 'Epitopes', 'Number_of_epitopes', 'Size_of_epitopes') > mat[,3] <- c(system.file('extdata','aggreg.judgement.tsv',package = 'Prize'), + system.file('extdata','tumor.pcm.tsv',package = 'Prize'), + system.file('extdata','normal.pcm.tsv',package = 'Prize'), + system.file('extdata','freq.pcm.tsv',package = 'Prize'), + system.file('extdata','epitope.pcm.tsv',package = 'Prize'), + system.file('extdata','epitopenum.pcm.tsv',package = 'Prize'), + system.file('extdata','epitopelength.pcm.tsv',package = 'Prize')) > # Computing alternatives priorities > prioritization <- pipeline(mat, model = 'relative', simulation = 500) The global priorities of decision elements can be visualized using the ahplot function. > ahplot(ahp_plot(prioritization), fontsize = 0.7, cradx = 0.11,sradx = 0.12, + cirx= 0.18, ciry = 0.07, dist = 0.06) 9
10 Prioritization_of_DE_genes Tumor_expression Normal_expression Frequency Epitopes Number_of_epitopes Size_of_epitopes Contribution of decision elements in the final priority estimation could also be visualized using wplot. > require(reshape2) > wplot(weight_plot(prioritization)$criteria_wplot, type = 'pie', + fontsize = 7, pcex = 3) 10
11 12% 7% Criteria Tumor_expression 47% Normal_expression 34% Frequency Epitopes The rainbow function illustrates prioritized alternatives detailing the contribution of each criterion in the final priority score. > rainbowplot(rainbow_plot(prioritization)$criteria_rainbowplot, xcex = 3) 11
12 FZD CD CD MUC Alternative EGFR 1956 CD Criteria Tumor_expression Normal_expression Frequency Epitopes BSG CD MUC CA Total priority score The Carbonic anhydrase 9 (CA9) and Mucin-16 (MUC16) with a global priority of are the alternative that contribute the most to the goal of choosing the optimal tumor marker/therapeutic target among the identified DE genes. Drugs targeting CA9 and MUC16 are currently in pre-clinical and clinical studies [6, 7]. > rainbow_plot(prioritization)$criteria_rainbowplot Tumor_expression Normal_expression Frequency Epitopes BSG CD CD CA MUC CD
13 EGFR CD MUC FZD total_priorities BSG CD CD CA MUC CD EGFR CD MUC FZD Rating AHP As the number of alternatives increase, the amount of pairwise comparison becomes large. Therefore, pairwise comparisons take much time and also the possibility of inconsistency in the comparisons increases. Rating AHP overcomes this problem by categorizing the criteria and/or subcriteria in order to classify alternatives. In another words, rating AHP uses a set of categories that serves as a base to evaluate the performance of the alternatives in terms of each criterion and/or subcriterion. The rating procedure is also suitable when the possibility of adding/removing alternatives exists. The rating AHP reduces the number of judgments that decision makers are required to make. The rating AHP differs from the relative AHP in the evaluation and obtaining the priority of alternatives. Hence, the decision markers define their decision problem, structure the problem into a hierarchy, and collect PCM matrices for each criteria/subcriteria similar to the relative AHP approach. Then, they use a rating approach to evaluate alternatives. 4.1 Defining a rating scale and obtaining alternatives priorities In the example scenario, the scientists would like to rank and prioritize 10 genes based on their potential role as a tumor marker/therapeutic target. To build a PCM matrix consisting of 10 alternatives 45 pairwise comparisons are required. The large number of pairwise comparisons makes this step time consuming and increase the possibility of inconsistency in the comparisons. Therefore, scientists decide to use rating AHP by defining a series of categories with respect to the criteria and/or subcriteria to evaluate alternatives. They also compute a PCM of these categories. For instance, they define two categories, single and multiple, for the numberof epitopes subcriteria, and compute their PCM. 13
14 > category_pcm = read.table(system.file('extdata','number.tsv', package = 'Prize') +, sep = '\t', header = TRUE, row.names = 1) > category_pcm Single Multiple Single 1 2 Multiple NA 1 Then, decision makers evaluate the alternatives against the defined categories and build an alternative matrix showing the category that each alternative belongs to. > alt_mat = read.table(system.file('extdata','numepitope_alternative_category.tsv', + package = 'Prize'), sep = '\t', header = FALSE) > alt_mat V1 V2 1 BSG 682 Single 2 CD Single 3 CD Single 4 CA9 768 Single 5 MUC Single 6 CD9 928 Multiple 7 EGFR 1956 Single 8 CD Single 9 MUC Single 10 FZD Multiple To compute the idealised priorities of alternatives, the rating functions can be called by a category PCM and an alternative matrix. > result = rating(category_pcm, alt_mat, simulation = 500) > # rated alternatives > RM(result) scale_category idealised_priorities BSG 682 "Single" "1" CD "Single" "1" CD "Single" "1" CA9 768 "Single" "1" MUC "Single" "1" CD9 928 "Multiple" "0.5" EGFR 1956 "Single" "1" CD "Single" "1" MUC "Single" "1" FZD "Multiple" "0.5" The matrix of idealised priorities (rated alternatives) can be used to call pipeline function to estimate final priorities of alternatives. 14
15 > mat <- matrix(nrow = 7, ncol = 3, data = NA) > mat[,1] <- c('0', '1','2','3','4','4.1','4.2') > mat[,2] <- c('prioritization_of_de_genes','tumor_expression','normal_expression', + 'Frequency', 'Epitopes', 'Number_of_epitopes', 'Size_of_epitopes') > mat[,3] <- c(system.file('extdata','aggreg.judgement.tsv',package = 'Prize'), + system.file('extdata','tumor_exp_rating.tsv',package = 'Prize'), + system.file('extdata','normal_exp_rating.tsv',package = 'Prize'), + system.file('extdata','freq_exp_rating.tsv',package = 'Prize'), + system.file('extdata','epitope.pcm.tsv',package = 'Prize'), + system.file('extdata','epitope_num_rating.tsv',package = 'Prize'), + system.file('extdata','epitope_size_rating.tsv',package = 'Prize')) > # Computing alternatives priorities > prioritization <- pipeline(mat, model = 'rating', simulation = 500) References [1] T.L. Saaty. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3): , [2] T.L. Saaty. Rank from comparisons and from ratings in the analytic hierarchy/network processes. European Journal of Operational Research, 168(2): , January [3] T.L. Saaty. The Analytic Hierarchy Process, Planning, Piority Setting, Resource Allocation. McGraw-Hill, New york, [4] E. Forman and K. Peniwati. Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal of Operational Research, 108(1): , [5] J.C. Gower. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53(3/4): , [6] M. Felder, A. Kapur, J. Gonzalez-Bosquet, S. Horibata, J. Heintz, R. Albrecht, L. Fass, J. Kaur, K. Hu, H. Shojaei, R. J. Whelan, and M. S. Patankar. MUC16 (CA125): tumor biomarker to cancer therapy, a work in progress. Mol. Cancer, 13:129, [7] P. C. McDonald, J. Y. Winum, C. T. Supuran, and S. Dedhar. Recent developments in targeting carbonic anhydrase IX for cancer therapeutics. Oncotarget, 3(1):84 97, Jan
Handling Partial Preferences in the Belief AHP Method: Application to Life Cycle Assessment
Handling Partial Preferences in the Belief AHP Method: Application to Life Cycle Assessment Amel Ennaceur 1, Zied Elouedi 1, and Eric Lefevre 2 1 University of Tunis, Institut Supérieur de Gestion de Tunis,
More informationSCIENCE & TECHNOLOGY
Pertanika J. Sci. & Technol. 25 (S): 241-254 (2017) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Fuzzy Lambda-Max Criteria Weight Determination for Feature Selection in Clustering
More informationA Multi-Criteria Decision Making Model for Treatment of Helicobacter pylori Infection in Children
HK J Paediatr (new series) 2012;17:237-242 A Multi-Criteria Decision Making Model for Treatment of Helicobacter pylori Infection in Children A ERJAEE, M BAGHERPOUR, S RAZEGHI, SM DEHGHANI, MH IMANIEH,
More informationAN ANALYTIC HIERARCHY PROCESS (AHP) APPROACH.
HOW DIFFERENT ARE THE PERCEPTIONS OVER THE MAIN MOTIVATIONAL TOOLS IN MILITARY? AN ANALYTIC HIERARCHY PROCESS (AHP) APPROACH. Paul-Mugurel POLEANSCHI Commander (Navy), Romanian Naval Forces Staff, Bucharest,
More informationAdvances in Environmental Biology
AENSI Journals Advances in Environmental Biology ISSN-1995-0756 EISSN-1998-1066 Journal home page: http://www.aensiweb.com/aeb/ Weighting the Behavioral Biases of Investors in Shiraz Stock Exchange by
More informationHow To Make A Decision: The Analytic Hierarchy Process
How To Make A Decision: The Analytic Hierarchy Process by Thomas L. Saaty 322 Mervis Hall University of Pittsburgh Pittsburgh, PA 5260 Policy makers at all levels of decision making in organizations use
More informationChapter 1. Introduction
Chapter 1 Introduction 1.1 Motivation and Goals The increasing availability and decreasing cost of high-throughput (HT) technologies coupled with the availability of computational tools and data form a
More informationA Study of Investigating Adolescents Eating out Behavior by Using Analytic Hierarchy Process
OPEN ACCESS EURASIA Journal of Mathematics Science and Technology Education ISSN: 1305-8223 (online) 1305-8215 (print) 2017 13(7):3227-3234 DOI 10.12973/eurasia.2017.00714a A Study of Investigating Adolescents
More informationASSESSMENT OF PHYSICIANS COLORECTAL CANCER SCREENING PRIORITIES USING THE ANALYTIC HIERARCHY PROCESS (AHP)
ASSESSMENT OF PHYSICIANS COLORECTAL CANCER SCREENING PRIORITIES USING THE ANALYTIC HIERARCHY PROCESS (AHP) James G. Dolan, MD * Department of Community and Preventive Medicine University of Rochester Rochester,
More informationThank you for your insightful comments and suggestions. It s really helpful to improve the manuscript.
Author s response to reviews Title: Application of the analytic hierarchy approach to the risk assessment of Zika virus disease transmission in Guangdong Province, China Authors: Xing Li (lixing.echo@foxmail.com)
More informationFundamental Concepts for Using Diagnostic Classification Models. Section #2 NCME 2016 Training Session. NCME 2016 Training Session: Section 2
Fundamental Concepts for Using Diagnostic Classification Models Section #2 NCME 2016 Training Session NCME 2016 Training Session: Section 2 Lecture Overview Nature of attributes What s in a name? Grain
More informationSome Considerations on The Application of the Analytic Hierarchy Process to the Travel Route Preference. Synopsis. 1. Introduction
Mem. Fae. Eng., Osaka City Univ., Vol. 36, pp. 47-52.(1995) Some Considerations on The Application of the Analytic Hierarchy Process to the Travel Route Preference by Qi ZHANG * and Takashi NISHIMURA **
More informationOn Eliciting Contribution Measures in Goal Models
On Eliciting Contribution Measures in Goal Models Sotirios Liaskos School of Information Technology York University Toronto, ON, Canada liaskos@yorku.ca Rina Jalman School of Information Technology York
More informationLOSS ANALYSIS OF MEDICAL FUNCTIONALITY DUE TO HOSPITAL S EARTHQUAKE-INDUCED DAMAGES
LOSS ANALYSIS OF MEDICAL FUNCTIONALITY DUE TO HOSPITAL S EARTHQUAKE-INDUCED DAMAGES K.C. Kuo, M. Banba 2, and Y. Suzuki 3 Postdoctoral researcher, Dept. of Architecture, National Cheng-Kung University,
More informationApplying the Analytic Hierarchy Process in healthcare research: A systematic literature review and evaluation of reporting
Schmidt et al. BMC Medical Informatics and Decision Making (2015) 15:112 DOI 10.1186/s12911-015-0234-7 RESEARCH ARTICLE Open Access Applying the Analytic Hierarchy Process in healthcare research: A systematic
More informationA Study of Methodology on Effective Feasibility Analysis Using Fuzzy & the Bayesian Network
, pp.116-120 http://dx.doi.org/10.14257/astl.2016.137.22 A Study of Methodology on Effective Feasibility Analysis Using Fuzzy & the Bayesian Network JaeHyuk Cho 1 1 Division of Next Growth Engine R&D Coordiantion,
More informationPrioritization of Organ Transplant Patients using Analytic Network Process
Proceedings of the 2014 Industrial and Systems Engineering Research Conference Y. Guan and H. Liao, eds. Prioritization of Organ Transplant Patients using Analytic Network Process Samira Abbasgholizadeh
More informationScienceDirect. A Multi-Criteria Decision Framework to Measure Spiritual Intelligence of University Teachers
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 91 (2016 ) 591 598 Information Technology and Quantitative Management (ITQM 2016) A Multi-Criteria Decision Framework to
More informationKatharina Schmidt 1*, Ana Babac 1, Frédéric Pauer 1, Kathrin Damm 1 and J-Matthias von der Schulenburg 1,2
Schmidt et al. Health Economics Review (2016) 6:50 DOI 10.1186/s13561-016-0130-6 RESEARCH Open Access Measuring patients priorities using the Analytic Hierarchy Process in comparison with Best-Worst-Scaling
More informationAn analysis of several novel frameworks and models in the consensus reaching process. Hengjie Zhang
An analysis of several novel frameworks and models in the consensus reaching process Hengjie Zhang Outline Background: consensus reaching process Model I: consensus with minimum adjustments Model II: consensus
More informationwas also my mentor, teacher, colleague, and friend. It is tempting to review John Horn s main contributions to the field of intelligence by
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179 185. (3362 citations in Google Scholar as of 4/1/2016) Who would have thought that a paper
More informationHow Does Analysis of Competing Hypotheses (ACH) Improve Intelligence Analysis?
How Does Analysis of Competing Hypotheses (ACH) Improve Intelligence Analysis? Richards J. Heuer, Jr. Version 1.2, October 16, 2005 This document is from a collection of works by Richards J. Heuer, Jr.
More informationReveal Relationships in Categorical Data
SPSS Categories 15.0 Specifications Reveal Relationships in Categorical Data Unleash the full potential of your data through perceptual mapping, optimal scaling, preference scaling, and dimension reduction
More informationApplication of Analytical Hierarchy Process and Bayesian Belief Networks for Risk Analysis
Volume 2 Application of Analytical Hierarchy Process and Bayesian Belief Networks for Risk Analysis A. Ahmed, R. Kusumo 2, S Savci 3, B. Kayis, M. Zhou 2 and Y.B. Khoo 2 School of Mechanical and Manufacturing
More informationExamining users preferences towards vertical graphical toolbars in simple search and point tasks
This is the preprint version of the following article: Michalski R. (2011). Examining users preferences towards vertical graphical toolbars in simple search and point tasks, Computers in Human Behavior,
More informationContact. Download handouts here: Hand outs. Fundamentals of evaluaeon. Benefit- risk analysis 23/05/2011
Contact PATIENT- CENTERED BENEFIT- RISK ANALYSIS: the case for AnalyEc Hierarchy Process ISPOR Workshop W3 Monday May, 3 011 Maarten J. IJzerman, PhD John F.P. Bridges, PhD Maarten J. IJzerman, PhD University
More informationSpeaker Notes: Qualitative Comparative Analysis (QCA) in Implementation Studies
Speaker Notes: Qualitative Comparative Analysis (QCA) in Implementation Studies PART 1: OVERVIEW Slide 1: Overview Welcome to Qualitative Comparative Analysis in Implementation Studies. This narrated powerpoint
More informationC/S/E/L :2008. On Analytical Rigor A Study of How Professional Intelligence Analysts Assess Rigor. innovations at the intersection of people,
C/S/E/L :2008 innovations at the intersection of people, technology, and work. On Analytical Rigor A Study of How Professional Intelligence Analysts Assess Rigor Daniel J. Zelik The Ohio State University
More informationThe use of Interpretive Narratives in the analysis of Project Failures. (Lynette Drevin)
Information Systems Research Methodology: From Paradigm to Practice (Roelien Goede) The use of Interpretive Narratives in the analysis of Project Failures. (Lynette Drevin) Measuring Information Security
More information3rd World Conference on Psychology, Counselling and Guidance (WCPCG-2012)
Available online at www.sciencedirect.com Procedia - Social and Behavioral Scien ce s 84 ( 2013 ) 1750 1756 3rd World Conference on Psychology, Counselling and Guidance (WCPCG-2012) The Role of Folk Dance
More informationStated Preference Methods Research in Health Care Decision Making A Critical Review of Its Use in the European Regulatory Environment.
Stated Preference Methods Research in Health Care Decision Making A Critical Review of Its Use in the European Regulatory Environment. Kevin Marsh, Evidera Axel Mühlbacher, Hochschule Neubrandenburg Janine
More informationGrundlagen des Software Engineering Fundamentals of Software Engineering. Chapter 4.4: Software Application Engineering Requirements Management
Software Engineering Research Group: es and Measurement Fachbereich Informatik TU Kaiserslautern Grundlagen des Software Engineering Fundamentals of Software Engineering Winter Term 2011/12 Prof. Dr. Dr.
More informationIssues That Should Not Be Overlooked in the Dominance Versus Ideal Point Controversy
Industrial and Organizational Psychology, 3 (2010), 489 493. Copyright 2010 Society for Industrial and Organizational Psychology. 1754-9426/10 Issues That Should Not Be Overlooked in the Dominance Versus
More informationAnswers to end of chapter questions
Answers to end of chapter questions Chapter 1 What are the three most important characteristics of QCA as a method of data analysis? QCA is (1) systematic, (2) flexible, and (3) it reduces data. What are
More informationUse of the Analytic Hierarchy Process (AHP) for examining healthcare professionals
Use of the Analytic Hierarchy Process (AHP) for examining healthcare professionals assessments of the relative importance of risk factors for falls in community-dwelling older people. Leandro Pecchia 1,2,
More informationCHAPTER 3 RESEARCH METHODOLOGY
CHAPTER 3 RESEARCH METHODOLOGY 3.1 Introduction 3.1 Methodology 3.1.1 Research Design 3.1. Research Framework Design 3.1.3 Research Instrument 3.1.4 Validity of Questionnaire 3.1.5 Statistical Measurement
More informationSummary. 20 May 2014 EMA/CHMP/SAWP/298348/2014 Procedure No.: EMEA/H/SAB/037/1/Q/2013/SME Product Development Scientific Support Department
20 May 2014 EMA/CHMP/SAWP/298348/2014 Procedure No.: EMEA/H/SAB/037/1/Q/2013/SME Product Development Scientific Support Department evaluating patients with Autosomal Dominant Polycystic Kidney Disease
More informationStatistical Audit. Summary. Conceptual and. framework. MICHAELA SAISANA and ANDREA SALTELLI European Commission Joint Research Centre (Ispra, Italy)
Statistical Audit MICHAELA SAISANA and ANDREA SALTELLI European Commission Joint Research Centre (Ispra, Italy) Summary The JRC analysis suggests that the conceptualized multi-level structure of the 2012
More informationInternational Journal of Engineering Research-Online A Peer Reviewed International Journal
RESEARCH ARTICLE ISSN: 2321-7758 EXPERT SYSTEM USING AHP TO SUGGEST DIET TO THE DIABETICS IN ORDER TO CONTROL BLOOD SUGAR LEVEL OF THE PATIENT BARMURA YIBGETA SIFIR 1, RAHUL RAGHVENDRA JOSHI 2 1 M.Tech,
More informationA Survey of Techniques for Optimizing Multiresponse Experiments
A Survey of Techniques for Optimizing Multiresponse Experiments Flavio S. Fogliatto, Ph.D. PPGEP / UFRGS Praça Argentina, 9/ Sala 402 Porto Alegre, RS 90040-020 Abstract Most industrial processes and products
More informationTRUST, MISTRUST AND DISTRUST IN ALLIANCING
TRUST, MISTRUST AND DISTRUST IN ALLIANCING Ling-Ye She 1 Faculty of Architecture, Building and Planning, The University of Melbourne,33 Lincoln Square South, Carlton, 3010, Victoria, Australia Despite
More informationGrounding Ontologies in the External World
Grounding Ontologies in the External World Antonio CHELLA University of Palermo and ICAR-CNR, Palermo antonio.chella@unipa.it Abstract. The paper discusses a case study of grounding an ontology in the
More informationCaveats for the Spatial Arrangement Method: Supplementary Materials
Caveats for the Spatial Arrangement Method: Supplementary Materials Steven Verheyen, Wouter Voorspoels, Wolf Vanpaemel, & Gert Storms University of Leuven, Leuven, Belgium 2 Data Our comment is based on
More informationCHAPTER 3 DATA ANALYSIS: DESCRIBING DATA
Data Analysis: Describing Data CHAPTER 3 DATA ANALYSIS: DESCRIBING DATA In the analysis process, the researcher tries to evaluate the data collected both from written documents and from other sources such
More informationDevelopment of self efficacy and attitude toward analytic geometry scale (SAAG-S)
Available online at www.sciencedirect.com Procedia - Social and Behavioral Sciences 55 ( 2012 ) 20 27 INTERNATIONAL CONFERENCE ON NEW HORIZONS IN EDUCATION INTE2012 Development of self efficacy and attitude
More informationTwo-stage Methods to Implement and Analyze the Biomarker-guided Clinical Trail Designs in the Presence of Biomarker Misclassification
RESEARCH HIGHLIGHT Two-stage Methods to Implement and Analyze the Biomarker-guided Clinical Trail Designs in the Presence of Biomarker Misclassification Yong Zang 1, Beibei Guo 2 1 Department of Mathematical
More informationImpact of Response Variability on Pareto Front Optimization
Impact of Response Variability on Pareto Front Optimization Jessica L. Chapman, 1 Lu Lu 2 and Christine M. Anderson-Cook 3 1 Department of Mathematics, Computer Science, and Statistics, St. Lawrence University,
More informationPERSONAL SALES PROCESS VIA FACTOR ANALYSIS
PERSONAL SALES PROCESS VIA FACTOR ANALYSIS David Říha Elena Říhová Abstract The main aim of this paper is to identify the personal factors of the salesman, which are more relevant to achieving sales success.
More informationDiscriminant Analysis with Categorical Data
- AW)a Discriminant Analysis with Categorical Data John E. Overall and J. Arthur Woodward The University of Texas Medical Branch, Galveston A method for studying relationships among groups in terms of
More informationModeling Crowd Behavior Based on Social Comparison Theory: Extended Abstract
Modeling Crowd Behavior Based on Social Comparison Theory: Extended Abstract Natalie Fridman and Gal Kaminka Bar Ilan University, Israel The MAVERICK Group Computer Science Department {fridman,galk}cs.biu.ac.il
More informationMeasurement and meaningfulness in Decision Modeling
Measurement and meaningfulness in Decision Modeling Brice Mayag University Paris Dauphine LAMSADE FRANCE Chapter 2 Brice Mayag (LAMSADE) Measurement theory and meaningfulness Chapter 2 1 / 47 Outline 1
More informationLecture (chapter 1): Introduction
Lecture (chapter 1): Introduction Ernesto F. L. Amaral January 17, 2018 Advanced Methods of Social Research (SOCI 420) Source: Healey, Joseph F. 2015. Statistics: A Tool for Social Research. Stamford:
More informationMethodology for Prioritizing Severe Emerging Diseases for Research and Development
Methodology for Prioritizing Severe Emerging Diseases for Research and Development Background At the request of its 194 Member States in May 2015, the World Health Organization (WHO) convened a broad coalition
More informationPreference Ratios in Multiattribute Evaluation (PRIME) Elicitation and Decision Procedures Under Incomplete Information
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART A: SYSTEMS AND HUMANS, VOL. 31, NO. 6, NOVEMBER 2001 533 Preference Ratios in Multiattribute Evaluation (PRIME) Elicitation and Decision Procedures
More informationGenomics Research. May 31, Malvika Pillai
Genomics Research May 31, 2018 Malvika Pillai Outline for Research Discussion Why Informatics Read journal articles! Example Paper Presentation Research Pipeline How To Read A Paper If I m aiming to just
More informationHuman health risk from trihalomethanes in drinking water evaluation with fuzzy aggregation
Environmental Exposure and Health 299 Human health risk from trihalomethanes in drinking water evaluation with fuzzy aggregation S. Chowdhury & T. Husain Memorial University of Newfoundland, St. John s,
More informationBIOGRAPHICAL SKETCH. Senior Trainer DEGREE. (if applicable)
BIOGRAPHICAL SKETCH Senior Trainer NAME: Patankar Manish Suresh era COMMONS USER NAME: patankar POSITION TITLE: Professor EDUCATION/TRAINING INSTITUTION AND LOCATION DEGREE YEAR(s) FIELD OF STUDY (if applicable)
More informationRational or Irrational?
1. a. Write three rational numbers. Rational or Irrational? b. Explain in your own words what a rational number is. 2. a. Write three irrational numbers. b. Explain in your own words what an irrational
More informationSupplementary Data. Correlation analysis. Importance of normalizing indices before applying SPCA
Supplementary Data Correlation analysis The correlation matrix R of the m = 25 GV indices calculated for each dataset is reported below (Tables S1 S3). R is an m m symmetric matrix, whose entries r ij
More informationEcological Statistics
A Primer of Ecological Statistics Second Edition Nicholas J. Gotelli University of Vermont Aaron M. Ellison Harvard Forest Sinauer Associates, Inc. Publishers Sunderland, Massachusetts U.S.A. Brief Contents
More informationIndividual Differences in Attention During Category Learning
Individual Differences in Attention During Category Learning Michael D. Lee (mdlee@uci.edu) Department of Cognitive Sciences, 35 Social Sciences Plaza A University of California, Irvine, CA 92697-5 USA
More informationPYSC 333 Psychology of Personality
PYSC 333 Psychology of Personality Session 5 Humanistic Theory of Personality- Part 1 Lecturer:, Dept. of Psychology Contact Information: mamankwah-poku@ug.edu.gh College of Education School of Continuing
More informationUpdate from IMI PREFER: Patient preferences in benefitrisk assessments during the medical product lifecycle
1 1 Update from IMI PREFER: Patient preferences in benefitrisk assessments during the medical product lifecycle Presenter: Chiara Whichello on behalf of the PREFER consortium June 5, 2018 The statements
More informationOpen Access Fuzzy Analytic Hierarchy Process-based Chinese Resident Best Fitness Behavior Method Research
Send Orders for Reprints to reprints@benthamscience.ae The Open Biomedical Engineering Journal, 0, 9, - Open Access Fuzzy Analytic Hierarchy Process-based Chinese Resident Best Fitness Behavior Method
More informationCHAPTER VI RESEARCH METHODOLOGY
CHAPTER VI RESEARCH METHODOLOGY 6.1 Research Design Research is an organized, systematic, data based, critical, objective, scientific inquiry or investigation into a specific problem, undertaken with the
More informationAdjustments for Rater Effects in
Adjustments for Rater Effects in Performance Assessment Walter M. Houston, Mark R. Raymond, and Joseph C. Svec American College Testing Alternative methods to correct for rater leniency/stringency effects
More informationCHAPTER 4 THE QUESTIONNAIRE DESIGN /SOLUTION DESIGN. This chapter contains explanations that become a basic knowledge to create a good
CHAPTER 4 THE QUESTIONNAIRE DESIGN /SOLUTION DESIGN This chapter contains explanations that become a basic knowledge to create a good questionnaire which is able to meet its objective. Just like the thesis
More informationData and Statistics 101: Key Concepts in the Collection, Analysis, and Application of Child Welfare Data
TECHNICAL REPORT Data and Statistics 101: Key Concepts in the Collection, Analysis, and Application of Child Welfare Data CONTENTS Executive Summary...1 Introduction...2 Overview of Data Analysis Concepts...2
More informationOn the convergence of multiattribute weighting methods
European Journal of Operational Research 129 (2001) 569±585 www.elsevier.com/locate/dsw Theory and Methodology On the convergence of multiattribute weighting methods Mari Poyhonen, Raimo P. Hamalainen
More informationEquilibrium Selection In Coordination Games
Equilibrium Selection In Coordination Games Presenter: Yijia Zhao (yz4k@virginia.edu) September 7, 2005 Overview of Coordination Games A class of symmetric, simultaneous move, complete information games
More informationCitation for published version (APA): Ebbes, P. (2004). Latent instrumental variables: a new approach to solve for endogeneity s.n.
University of Groningen Latent instrumental variables Ebbes, P. IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document
More informationThis memo includes background of the project, the major themes within the comments, and specific issues that require further CSAC/HITAC action.
TO: FR: RE: Consensus Standards Approval Committee (CSAC) Health IT Advisory Committee (HITAC) Helen Burstin, Karen Pace, and Christopher Millet Comments Received on Draft Report: Review and Update of
More informationChapter 1: Explaining Behavior
Chapter 1: Explaining Behavior GOAL OF SCIENCE is to generate explanations for various puzzling natural phenomenon. - Generate general laws of behavior (psychology) RESEARCH: principle method for acquiring
More informationComparison of different approaches applied in Analytic Hierarchy Process an example of information needs of patients with rare diseases
Pauer et al. BMC Medical Informatics and Decision Making (2016) 16:117 DOI 10.1186/s12911-016-0346-8 RESEARCH ARTICLE Comparison of different approaches applied in Analytic Hierarchy Process an example
More informationIDEA Technical Report No. 20. Updated Technical Manual for the IDEA Feedback System for Administrators. Stephen L. Benton Dan Li
IDEA Technical Report No. 20 Updated Technical Manual for the IDEA Feedback System for Administrators Stephen L. Benton Dan Li July 2018 2 Table of Contents Introduction... 5 Sample Description... 6 Response
More informationBayesian and Frequentist Approaches
Bayesian and Frequentist Approaches G. Jogesh Babu Penn State University http://sites.stat.psu.edu/ babu http://astrostatistics.psu.edu All models are wrong But some are useful George E. P. Box (son-in-law
More informationDeveloping an ethical framework for One Health policy analysis: suggested first steps
Developing an ethical framework for One Health policy analysis: suggested first steps Joshua Freeman, Clinical Microbiologist, Canterbury DHB Professor John McMillan Bioethics Centre, University of Otago
More informationPOST GRADUATE DIPLOMA IN BIOETHICS (PGDBE) Term-End Examination June, 2016 MHS-014 : RESEARCH METHODOLOGY
No. of Printed Pages : 12 MHS-014 POST GRADUATE DIPLOMA IN BIOETHICS (PGDBE) Term-End Examination June, 2016 MHS-014 : RESEARCH METHODOLOGY Time : 2 hours Maximum Marks : 70 PART A Attempt all questions.
More informationDevelopment and Psychometric Properties of the Relational Mobility Scale for the Indonesian Population
Development and Psychometric Properties of the Relational Mobility Scale for the Indonesian Population Sukaesi Marianti Abstract This study aims to develop the Relational Mobility Scale for the Indonesian
More informationMCAS Equating Research Report: An Investigation of FCIP-1, FCIP-2, and Stocking and. Lord Equating Methods 1,2
MCAS Equating Research Report: An Investigation of FCIP-1, FCIP-2, and Stocking and Lord Equating Methods 1,2 Lisa A. Keller, Ronald K. Hambleton, Pauline Parker, Jenna Copella University of Massachusetts
More informationGeneral recognition theory of categorization: A MATLAB toolbox
ehavior Research Methods 26, 38 (4), 579-583 General recognition theory of categorization: MTL toolbox LEOL. LFONSO-REESE San Diego State University, San Diego, California General recognition theory (GRT)
More informationAn evidence rating scale for New Zealand
Social Policy Evaluation and Research Unit An evidence rating scale for New Zealand Understanding the effectiveness of interventions in the social sector Using Evidence for Impact MARCH 2017 About Superu
More informationSOCIAL AND CULTURAL ANTHROPOLOGY
SOCIAL AND CULTURAL ANTHROPOLOGY Overall grade boundaries Grade: E D C B A Mark range: 0-7 8-15 16-22 23-28 29-36 The range and suitability of the work submitted In reading over the comments of examiners
More informationA SAS Macro for Adaptive Regression Modeling
A SAS Macro for Adaptive Regression Modeling George J. Knafl, PhD Professor University of North Carolina at Chapel Hill School of Nursing Supported in part by NIH Grants R01 AI57043 and R03 MH086132 Overview
More informationIdeals Aren t Always Typical: Dissociating Goodness-of-Exemplar From Typicality Judgments
Ideals Aren t Always Typical: Dissociating Goodness-of-Exemplar From Typicality Judgments Aniket Kittur (nkittur@ucla.edu) Keith J. Holyoak (holyoak@lifesci.ucla.edu) Department of Psychology, University
More informationSample Sizes for Predictive Regression Models and Their Relationship to Correlation Coefficients
Sample Sizes for Predictive Regression Models and Their Relationship to Correlation Coefficients Gregory T. Knofczynski Abstract This article provides recommended minimum sample sizes for multiple linear
More informationField-normalized citation impact indicators and the choice of an appropriate counting method
Field-normalized citation impact indicators and the choice of an appropriate counting method Ludo Waltman and Nees Jan van Eck Centre for Science and Technology Studies, Leiden University, The Netherlands
More informationStudy on Improving the Worker Safety at Roadway Worksites in Japan
(7,473 Words: 4,3 texts, 10 figures, 3 tables) Study on Improving the Worker Safety at Roadway Worksites in Japan Submitted by Masayuki Hirasawa Senior Researcher Traffic Engineering Research Team, Civil
More informationFunnelling Used to describe a process of narrowing down of focus within a literature review. So, the writer begins with a broad discussion providing b
Accidental sampling A lesser-used term for convenience sampling. Action research An approach that challenges the traditional conception of the researcher as separate from the real world. It is associated
More informationLecturer: Dr. Adote Anum, Dept. of Psychology Contact Information:
Lecturer: Dr. Adote Anum, Dept. of Psychology Contact Information: aanum@ug.edu.gh College of Education School of Continuing and Distance Education 2014/2015 2016/2017 Session Overview The course provides
More informationThe Classification Accuracy of Measurement Decision Theory. Lawrence Rudner University of Maryland
Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago, April 23-25, 2003 The Classification Accuracy of Measurement Decision Theory Lawrence Rudner University
More informationlinking in educational measurement: Taking differential motivation into account 1
Selecting a data collection design for linking in educational measurement: Taking differential motivation into account 1 Abstract In educational measurement, multiple test forms are often constructed to
More informationThe Role of Feedback in Categorisation
The Role of in Categorisation Mark Suret (m.suret@psychol.cam.ac.uk) Department of Experimental Psychology; Downing Street Cambridge, CB2 3EB UK I.P.L. McLaren (iplm2@cus.cam.ac.uk) Department of Experimental
More informationRemarks on Bayesian Control Charts
Remarks on Bayesian Control Charts Amir Ahmadi-Javid * and Mohsen Ebadi Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran * Corresponding author; email address: ahmadi_javid@aut.ac.ir
More informationHow Do We Assess Students in the Interpreting Examinations?
How Do We Assess Students in the Interpreting Examinations? Fred S. Wu 1 Newcastle University, United Kingdom The field of assessment in interpreter training is under-researched, though trainers and researchers
More informationCochrane Pregnancy and Childbirth Group Methodological Guidelines
Cochrane Pregnancy and Childbirth Group Methodological Guidelines [Prepared by Simon Gates: July 2009, updated July 2012] These guidelines are intended to aid quality and consistency across the reviews
More informationTitle: Identifying work ability promoting factors for home care aides and assistant nurses
Author's response to reviews Title: Identifying work ability promoting factors for home care aides and assistant nurses Authors: Agneta Larsson (agneta.larsson@ltu.se) Lena Karlqvist (lena.karlqvist@ltu.se)
More informationJohn B. Lee. B.Sc, The University of Toronto, 1976 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF
A N I N T E R A C T I V E M U L T I - C R I T E R I A D E C I S I O N M O D E L I N G T O O L U S I N G T H E A N A L Y T I C H I E R A R C H Y P R O C E S S M E T H O D O L O G Y by John B. Lee B.Sc,
More informationSupplementary Materials Are taboo words simply more arousing?
Supplementary Materials Are taboo words simply more arousing? Our study was not designed to compare potential differences on memory other than arousal between negative and taboo words. Thus, our data cannot
More informationIit Istituto di Informatica e Telematica
C Consiglio Nazionale delle Ricerche Improved Automatic Maturity Assessment of Wikipedia Medical Articles E. Marzini, A. Spognardi, I. Matteucci, P. Mori, M. Petrocchi, R. Conti IIT TR-11/2014 Technical
More information